A Bayesian Framework Allowing Incorporation of Retrospective Information in Prospective Diagnostic Biomarker-Validation Designs

Abstract

The sample size of a prospective clinical study aimed at validation of a diagnostic biomarker-based test may be prohibitively large. We present a Bayesian framework that allows incorporating available development-study information about the performance of the test. As a result, the framework allows reducing the sample size required in the validation study, which may render the latter study feasible. The validation is based on the Bayesian testing of a hypothesis regarding possible values of AUC. Toward this end, first, available information is translated into a prior distribution. Next, this prior distribution is used in a Bayesian design to evaluate the performance of the diagnostic-test. We perform a simulation study to compare the power of the proposed Bayesian design to the approach ignoring development-study information. For each scenario, 1000 studies of sample size 100, 400, and 800 are simulated. Overall, the proposed Bayesian design leads to a substantially higher power than the flat-prior design. In some of the considered simulation settings, the Bayesian design requires as little as 50% of the flat-prior traditional design's sample size to reach approximately the same power. Moreover, a simulation-based application strategy is proposed and presented with respect to a case-study involving the development of a biomarker-based diagnostic-test for Alzheimer's disease.